Resolve typo: marged -> merged
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# SpanMarker for uncased Named Entity Recognition
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the underlying encoder. See [train.py](train.py) for the training script.
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It is trained on [P3ps/Cross_ner](https://huggingface.co/datasets/P3ps/Cross_ner), which I believe is a variant of [DFKI-SLT/cross_ner](https://huggingface.co/datasets/DFKI-SLT/cross_ner) that
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Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance:
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[tomaarsen/span-marker-bert-base-cross-ner](https://huggingface.co/tomaarsen/span-marker-bert-base-cross-ner).
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# SpanMarker for uncased Named Entity Recognition
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the underlying encoder. See [train.py](train.py) for the training script.
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It is trained on [P3ps/Cross_ner](https://huggingface.co/datasets/P3ps/Cross_ner), which I believe is a variant of [DFKI-SLT/cross_ner](https://huggingface.co/datasets/DFKI-SLT/cross_ner) that merged the validation set into the training set and applied deduplication.
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Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance:
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[tomaarsen/span-marker-bert-base-cross-ner](https://huggingface.co/tomaarsen/span-marker-bert-base-cross-ner).
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